Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations894
Missing cells15,342
Missing cells (%)46.4%
Total size in memory1.5 MiB
Average record size in memory1.7 KiB

Variable types

Text36
Unsupported1

Alerts

felt_sympathetic_to_victims has constant value "3.Undecided" Constant
impact_on_own_life has constant value "3.Undecided" Constant
impact_on_society has constant value "3.Undecided" Constant
nationality has 749 (83.8%) missing values Missing
gender_identity has 727 (81.3%) missing values Missing
age has 725 (81.1%) missing values Missing
education_level has 742 (83.0%) missing values Missing
visit_type has 735 (82.2%) missing values Missing
visit_purpose has 745 (83.3%) missing values Missing
religious has 739 (82.7%) missing values Missing
political_identity has 148 (16.6%) missing values Missing
visited_memorial_before has 746 (83.4%) missing values Missing
personal_connection_nazi_history has 742 (83.0%) missing values Missing
personal_connection_details has 865 (96.8%) missing values Missing
knowledge_ww2 has 148 (16.6%) missing values Missing
knowledge_bergen_belsen has 149 (16.7%) missing values Missing
knowledge_persecuted_jews has 150 (16.8%) missing values Missing
knowledge_other_persecuted_groups has 150 (16.8%) missing values Missing
known_persecuted_groups_open has 827 (92.5%) missing values Missing
technologies_used has 816 (91.3%) missing values Missing
felt_part_of_activity has 172 (19.2%) missing values Missing
involvement_over_irrelevant_thoughts has 173 (19.4%) missing values Missing
experienced_activity_feeling has 172 (19.2%) missing values Missing
lost_track_of_time has 172 (19.2%) missing values Missing
was_interesting has 174 (19.5%) missing values Missing
left_weak_impression has 174 (19.5%) missing values Missing
was_boring has 174 (19.5%) missing values Missing
thought_innovative has 174 (19.5%) missing values Missing
understood_camp_appearance has 173 (19.4%) missing values Missing
understood_life_in_camp has 173 (19.4%) missing values Missing
understood_camp_function has 173 (19.4%) missing values Missing
felt_sympathetic_to_victims has 175 (19.6%) missing values Missing
impact_on_own_life has 175 (19.6%) missing values Missing
impact_on_society has 175 (19.6%) missing values Missing
impact_society_details has 894 (100.0%) missing values Missing
want_to_share_learning has 169 (18.9%) missing values Missing
plan_to_learn_more has 169 (18.9%) missing values Missing
additional_feedback has 891 (99.7%) missing values Missing
email has 887 (99.2%) missing values Missing
impact_society_details is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-05-15 15:42:40.017980
Analysis finished2025-05-15 15:42:40.497549
Duration0.48 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

nationality
Text

Missing 

Distinct26
Distinct (%)17.9%
Missing749
Missing (%)83.8%
Memory size38.4 KiB
2025-05-15T17:42:40.625054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length7
Mean length7.275862069
Min length4

Characters and Unicode

Total characters1,055
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)13.1%

Sample

1st rowGermany
2nd rowPoland
3rd rowSpam
4th rowIreland
5th rowGermany
ValueCountFrequency (%)
germany 106
69.7%
united 6
 
3.9%
kingdom 5
 
3.3%
netherlands 4
 
2.6%
turkey 3
 
2.0%
spam 3
 
2.0%
poland 3
 
2.0%
france 2
 
1.3%
iran 1
 
0.7%
mexico 1
 
0.7%
Other values (18) 18
 
11.8%
2025-05-15T17:42:41.001566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 140
13.3%
n 137
13.0%
a 135
12.8%
r 126
11.9%
m 117
11.1%
y 109
10.3%
G 107
10.1%
i 20
 
1.9%
d 20
 
1.9%
t 14
 
1.3%
Other values (30) 130
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1055
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 140
13.3%
n 137
13.0%
a 135
12.8%
r 126
11.9%
m 117
11.1%
y 109
10.3%
G 107
10.1%
i 20
 
1.9%
d 20
 
1.9%
t 14
 
1.3%
Other values (30) 130
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1055
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 140
13.3%
n 137
13.0%
a 135
12.8%
r 126
11.9%
m 117
11.1%
y 109
10.3%
G 107
10.1%
i 20
 
1.9%
d 20
 
1.9%
t 14
 
1.3%
Other values (30) 130
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1055
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 140
13.3%
n 137
13.0%
a 135
12.8%
r 126
11.9%
m 117
11.1%
y 109
10.3%
G 107
10.1%
i 20
 
1.9%
d 20
 
1.9%
t 14
 
1.3%
Other values (30) 130
12.3%

gender_identity
Text

Missing 

Distinct5
Distinct (%)3.0%
Missing727
Missing (%)81.3%
Memory size38.6 KiB
2025-05-15T17:42:41.121056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length6
Mean length5.269461078
Min length4

Characters and Unicode

Total characters880
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowSpam
4th rowMale
5th rowMale
ValueCountFrequency (%)
female 79
44.1%
male 77
43.0%
spam 5
 
2.8%
prefer 4
 
2.2%
not 4
 
2.2%
to 4
 
2.2%
say 4
 
2.2%
other 2
 
1.1%
2025-05-15T17:42:41.385781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 245
27.8%
a 165
18.8%
l 156
17.7%
m 84
 
9.5%
F 79
 
9.0%
M 77
 
8.8%
12
 
1.4%
t 10
 
1.1%
r 10
 
1.1%
o 8
 
0.9%
Other values (9) 34
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 880
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 245
27.8%
a 165
18.8%
l 156
17.7%
m 84
 
9.5%
F 79
 
9.0%
M 77
 
8.8%
12
 
1.4%
t 10
 
1.1%
r 10
 
1.1%
o 8
 
0.9%
Other values (9) 34
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 880
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 245
27.8%
a 165
18.8%
l 156
17.7%
m 84
 
9.5%
F 79
 
9.0%
M 77
 
8.8%
12
 
1.4%
t 10
 
1.1%
r 10
 
1.1%
o 8
 
0.9%
Other values (9) 34
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 880
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 245
27.8%
a 165
18.8%
l 156
17.7%
m 84
 
9.5%
F 79
 
9.0%
M 77
 
8.8%
12
 
1.4%
t 10
 
1.1%
r 10
 
1.1%
o 8
 
0.9%
Other values (9) 34
 
3.9%

age
Text

Missing 

Distinct10
Distinct (%)5.9%
Missing725
Missing (%)81.1%
Memory size41.8 KiB
2025-05-15T17:42:41.545002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.136094675
Min length3

Characters and Unicode

Total characters868
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16–18
2nd row85+
3rd row16–18
4th row85+
5th row18–24
ValueCountFrequency (%)
16–18 29
15.9%
25–34 24
13.2%
45–54 24
13.2%
55–64 22
12.1%
35–44 20
11.0%
65–74 18
9.9%
under 13
7.1%
16 13
7.1%
18–24 9
 
4.9%
85 8
 
4.4%
2025-05-15T17:42:41.835674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 164
18.9%
4 163
18.8%
– 148
17.1%
6 82
9.4%
1 80
9.2%
8 48
 
5.5%
3 44
 
5.1%
2 33
 
3.8%
7 20
 
2.3%
U 13
 
1.5%
Other values (6) 73
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 868
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 164
18.9%
4 163
18.8%
– 148
17.1%
6 82
9.4%
1 80
9.2%
8 48
 
5.5%
3 44
 
5.1%
2 33
 
3.8%
7 20
 
2.3%
U 13
 
1.5%
Other values (6) 73
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 868
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 164
18.9%
4 163
18.8%
– 148
17.1%
6 82
9.4%
1 80
9.2%
8 48
 
5.5%
3 44
 
5.1%
2 33
 
3.8%
7 20
 
2.3%
U 13
 
1.5%
Other values (6) 73
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 868
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 164
18.9%
4 163
18.8%
– 148
17.1%
6 82
9.4%
1 80
9.2%
8 48
 
5.5%
3 44
 
5.1%
2 33
 
3.8%
7 20
 
2.3%
U 13
 
1.5%
Other values (6) 73
8.4%

education_level
Text

Missing 

Distinct6
Distinct (%)3.9%
Missing742
Missing (%)83.0%
Memory size40.5 KiB
2025-05-15T17:42:42.004757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length17
Mean length20.69078947
Min length9

Characters and Unicode

Total characters3,145
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecondary school
2nd rowSecondary school
3rd rowDoctorate
4th rowSecondary school
5th rowBachelor's degree
ValueCountFrequency (%)
school 57
14.8%
degree 45
11.7%
vocational 44
11.4%
or 44
11.4%
training 44
11.4%
apprenticeship 44
11.4%
high 34
8.8%
bachelor's 23
6.0%
secondary 23
6.0%
master's 22
 
5.7%
2025-05-15T17:42:42.283825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 304
 
9.7%
e 297
 
9.4%
i 254
 
8.1%
r 251
 
8.0%
a 250
 
7.9%
234
 
7.4%
n 199
 
6.3%
c 197
 
6.3%
s 168
 
5.3%
t 166
 
5.3%
Other values (13) 825
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 304
 
9.7%
e 297
 
9.4%
i 254
 
8.1%
r 251
 
8.0%
a 250
 
7.9%
234
 
7.4%
n 199
 
6.3%
c 197
 
6.3%
s 168
 
5.3%
t 166
 
5.3%
Other values (13) 825
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 304
 
9.7%
e 297
 
9.4%
i 254
 
8.1%
r 251
 
8.0%
a 250
 
7.9%
234
 
7.4%
n 199
 
6.3%
c 197
 
6.3%
s 168
 
5.3%
t 166
 
5.3%
Other values (13) 825
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 304
 
9.7%
e 297
 
9.4%
i 254
 
8.1%
r 251
 
8.0%
a 250
 
7.9%
234
 
7.4%
n 199
 
6.3%
c 197
 
6.3%
s 168
 
5.3%
t 166
 
5.3%
Other values (13) 825
26.2%

visit_type
Text

Missing 

Distinct7
Distinct (%)4.4%
Missing735
Missing (%)82.2%
Memory size40.9 KiB
2025-05-15T17:42:42.455936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length26
Mean length21.44654088
Min length4

Characters and Unicode

Total characters3,410
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowAs a student with my group
2nd rowAlone
3rd rowAs a student with my group
4th rowAs an adult with other adults
5th rowAs an adult with children
ValueCountFrequency (%)
as 117
16.1%
with 117
16.1%
an 77
10.6%
adult 77
10.6%
other 70
9.6%
adults 60
8.3%
a 40
 
5.5%
my 40
 
5.5%
student 39
 
5.4%
group 39
 
5.4%
Other values (5) 51
7.0%
2025-05-15T17:42:42.745007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
568
16.7%
t 403
11.8%
a 258
 
7.6%
s 217
 
6.4%
u 215
 
6.3%
h 204
 
6.0%
d 194
 
5.7%
l 183
 
5.4%
n 163
 
4.8%
e 156
 
4.6%
Other values (13) 849
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
568
16.7%
t 403
11.8%
a 258
 
7.6%
s 217
 
6.4%
u 215
 
6.3%
h 204
 
6.0%
d 194
 
5.7%
l 183
 
5.4%
n 163
 
4.8%
e 156
 
4.6%
Other values (13) 849
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
568
16.7%
t 403
11.8%
a 258
 
7.6%
s 217
 
6.4%
u 215
 
6.3%
h 204
 
6.0%
d 194
 
5.7%
l 183
 
5.4%
n 163
 
4.8%
e 156
 
4.6%
Other values (13) 849
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
568
16.7%
t 403
11.8%
a 258
 
7.6%
s 217
 
6.4%
u 215
 
6.3%
h 204
 
6.0%
d 194
 
5.7%
l 183
 
5.4%
n 163
 
4.8%
e 156
 
4.6%
Other values (13) 849
24.9%

visit_purpose
Text

Missing 

Distinct13
Distinct (%)8.7%
Missing745
Missing (%)83.3%
Memory size41.9 KiB
2025-05-15T17:42:42.925299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length71
Median length31
Mean length30.89932886
Min length5

Characters and Unicode

Total characters4,604
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)3.4%

Sample

1st rowTo learn more about the history
2nd rowOther
3rd rowTo learn more about the history
4th rowTo learn more about the history
5th rowTo learn more about the history
ValueCountFrequency (%)
to 116
14.9%
learn 116
14.9%
more 116
14.9%
about 116
14.9%
the 116
14.9%
history 86
11.0%
commemoration 32
 
4.1%
history__for 28
 
3.6%
for 18
 
2.3%
other 15
 
1.9%
Other values (6) 22
 
2.8%
2025-05-15T17:42:43.264495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 636
13.8%
632
13.7%
r 485
10.5%
e 433
9.4%
t 409
8.9%
a 284
 
6.2%
h 265
 
5.8%
m 236
 
5.1%
i 156
 
3.4%
n 156
 
3.4%
Other values (10) 912
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 636
13.8%
632
13.7%
r 485
10.5%
e 433
9.4%
t 409
8.9%
a 284
 
6.2%
h 265
 
5.8%
m 236
 
5.1%
i 156
 
3.4%
n 156
 
3.4%
Other values (10) 912
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 636
13.8%
632
13.7%
r 485
10.5%
e 433
9.4%
t 409
8.9%
a 284
 
6.2%
h 265
 
5.8%
m 236
 
5.1%
i 156
 
3.4%
n 156
 
3.4%
Other values (10) 912
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 636
13.8%
632
13.7%
r 485
10.5%
e 433
9.4%
t 409
8.9%
a 284
 
6.2%
h 265
 
5.8%
m 236
 
5.1%
i 156
 
3.4%
n 156
 
3.4%
Other values (10) 912
19.8%

religious
Text

Missing 

Distinct3
Distinct (%)1.9%
Missing739
Missing (%)82.7%
Memory size38.1 KiB
2025-05-15T17:42:43.409964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length3
Mean length3.922580645
Min length2

Characters and Unicode

Total characters608
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowI don't know
4th rowI don't know
5th rowYes
ValueCountFrequency (%)
yes 68
33.8%
no 64
31.8%
i 23
 
11.4%
don't 23
 
11.4%
know 23
 
11.4%
2025-05-15T17:42:43.632006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 110
18.1%
Y 68
11.2%
e 68
11.2%
s 68
11.2%
N 64
10.5%
46
7.6%
n 46
7.6%
I 23
 
3.8%
d 23
 
3.8%
' 23
 
3.8%
Other values (3) 69
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 110
18.1%
Y 68
11.2%
e 68
11.2%
s 68
11.2%
N 64
10.5%
46
7.6%
n 46
7.6%
I 23
 
3.8%
d 23
 
3.8%
' 23
 
3.8%
Other values (3) 69
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 110
18.1%
Y 68
11.2%
e 68
11.2%
s 68
11.2%
N 64
10.5%
46
7.6%
n 46
7.6%
I 23
 
3.8%
d 23
 
3.8%
' 23
 
3.8%
Other values (3) 69
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 110
18.1%
Y 68
11.2%
e 68
11.2%
s 68
11.2%
N 64
10.5%
46
7.6%
n 46
7.6%
I 23
 
3.8%
d 23
 
3.8%
' 23
 
3.8%
Other values (3) 69
11.3%

political_identity
Text

Missing 

Distinct5
Distinct (%)0.7%
Missing148
Missing (%)16.6%
Memory size53.7 KiB
2025-05-15T17:42:43.768768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length8
Mean length8.713136729
Min length8

Characters and Unicode

Total characters6,500
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Centre
2nd row3.Centre
3rd row3.Centre
4th row3.Centre
5th row3.Centre
ValueCountFrequency (%)
3.centre 659
74.2%
of 55
 
6.2%
centre 55
 
6.2%
2.left 44
 
5.0%
5.far 17
 
1.9%
right 17
 
1.9%
1.far 15
 
1.7%
left 15
 
1.7%
4.right 11
 
1.2%
2025-05-15T17:42:44.014408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1487
22.9%
t 801
12.3%
r 763
11.7%
. 746
11.5%
n 714
11.0%
C 659
10.1%
3 659
10.1%
142
 
2.2%
f 114
 
1.8%
c 55
 
0.8%
Other values (13) 360
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1487
22.9%
t 801
12.3%
r 763
11.7%
. 746
11.5%
n 714
11.0%
C 659
10.1%
3 659
10.1%
142
 
2.2%
f 114
 
1.8%
c 55
 
0.8%
Other values (13) 360
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1487
22.9%
t 801
12.3%
r 763
11.7%
. 746
11.5%
n 714
11.0%
C 659
10.1%
3 659
10.1%
142
 
2.2%
f 114
 
1.8%
c 55
 
0.8%
Other values (13) 360
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1487
22.9%
t 801
12.3%
r 763
11.7%
. 746
11.5%
n 714
11.0%
C 659
10.1%
3 659
10.1%
142
 
2.2%
f 114
 
1.8%
c 55
 
0.8%
Other values (13) 360
 
5.5%

visited_memorial_before
Text

Missing 

Distinct3
Distinct (%)2.0%
Missing746
Missing (%)83.4%
Memory size39.4 KiB
2025-05-15T17:42:44.177908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length16
Mean length14.10810811
Min length2

Characters and Unicode

Total characters2,088
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes, once before
2nd rowYes, more than once before
3rd rowNo
4th rowYes, more than once before
5th rowYes, once before
ValueCountFrequency (%)
yes 93
21.5%
before 93
21.5%
once 93
21.5%
no 55
12.7%
than 49
11.3%
more 49
11.3%
2025-05-15T17:42:44.480130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 421
20.2%
o 290
13.9%
284
13.6%
r 142
 
6.8%
n 142
 
6.8%
s 93
 
4.5%
, 93
 
4.5%
Y 93
 
4.5%
f 93
 
4.5%
b 93
 
4.5%
Other values (6) 344
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 421
20.2%
o 290
13.9%
284
13.6%
r 142
 
6.8%
n 142
 
6.8%
s 93
 
4.5%
, 93
 
4.5%
Y 93
 
4.5%
f 93
 
4.5%
b 93
 
4.5%
Other values (6) 344
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 421
20.2%
o 290
13.9%
284
13.6%
r 142
 
6.8%
n 142
 
6.8%
s 93
 
4.5%
, 93
 
4.5%
Y 93
 
4.5%
f 93
 
4.5%
b 93
 
4.5%
Other values (6) 344
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 421
20.2%
o 290
13.9%
284
13.6%
r 142
 
6.8%
n 142
 
6.8%
s 93
 
4.5%
, 93
 
4.5%
Y 93
 
4.5%
f 93
 
4.5%
b 93
 
4.5%
Other values (6) 344
16.5%

personal_connection_nazi_history
Text

Missing 

Distinct3
Distinct (%)2.0%
Missing742
Missing (%)83.0%
Memory size37.8 KiB
2025-05-15T17:42:44.564169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.210526316
Min length2

Characters and Unicode

Total characters336
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowYes
4th rowNo
5th rowYes
ValueCountFrequency (%)
no 120
78.9%
yes 32
 
21.1%
2025-05-15T17:42:44.784701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 120
35.7%
N 102
30.4%
Y 32
 
9.5%
e 32
 
9.5%
s 32
 
9.5%
n 18
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 120
35.7%
N 102
30.4%
Y 32
 
9.5%
e 32
 
9.5%
s 32
 
9.5%
n 18
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 120
35.7%
N 102
30.4%
Y 32
 
9.5%
e 32
 
9.5%
s 32
 
9.5%
n 18
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 120
35.7%
N 102
30.4%
Y 32
 
9.5%
e 32
 
9.5%
s 32
 
9.5%
n 18
 
5.4%

personal_connection_details
Text

Missing 

Distinct12
Distinct (%)41.4%
Missing865
Missing (%)96.8%
Memory size36.1 KiB
2025-05-15T17:42:44.975670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length35
Mean length24.27586207
Min length4

Characters and Unicode

Total characters704
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)13.8%

Sample

1st rowPrefer not to say
2nd rowI am a survivor of Nazi persecution
3rd rowAncestors were displaced
4th rowRelatives were in the resistance
5th rowRelatives were perpetrators
ValueCountFrequency (%)
were 18
17.8%
relatives 14
 
13.9%
persecuted 5
 
5.0%
perpetrators 5
 
5.0%
prefer 4
 
4.0%
to 4
 
4.0%
not 4
 
4.0%
say 4
 
4.0%
other 4
 
4.0%
the 4
 
4.0%
Other values (17) 35
34.7%
2025-05-15T17:42:45.356596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 140
19.9%
72
10.2%
r 66
 
9.4%
t 58
 
8.2%
s 50
 
7.1%
a 40
 
5.7%
i 36
 
5.1%
p 27
 
3.8%
o 24
 
3.4%
l 22
 
3.1%
Other values (20) 169
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 140
19.9%
72
10.2%
r 66
 
9.4%
t 58
 
8.2%
s 50
 
7.1%
a 40
 
5.7%
i 36
 
5.1%
p 27
 
3.8%
o 24
 
3.4%
l 22
 
3.1%
Other values (20) 169
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 140
19.9%
72
10.2%
r 66
 
9.4%
t 58
 
8.2%
s 50
 
7.1%
a 40
 
5.7%
i 36
 
5.1%
p 27
 
3.8%
o 24
 
3.4%
l 22
 
3.1%
Other values (20) 169
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 140
19.9%
72
10.2%
r 66
 
9.4%
t 58
 
8.2%
s 50
 
7.1%
a 40
 
5.7%
i 36
 
5.1%
p 27
 
3.8%
o 24
 
3.4%
l 22
 
3.1%
Other values (20) 169
24.0%

knowledge_ww2
Text

Missing 

Distinct5
Distinct (%)0.7%
Missing148
Missing (%)16.6%
Memory size55.3 KiB
2025-05-15T17:42:45.597248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length10
Mean length10.9155496
Min length10

Characters and Unicode

Total characters8,143
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Somewhat
2nd row3.Somewhat
3rd row3.Somewhat
4th row3.Somewhat
5th row3.Somewhat
ValueCountFrequency (%)
3.somewhat 638
68.8%
4.more 70
 
7.6%
than 70
 
7.6%
average 70
 
7.6%
5.very 29
 
3.1%
much 29
 
3.1%
2.very 6
 
0.6%
little 6
 
0.6%
1.not 3
 
0.3%
at 3
 
0.3%
2025-05-15T17:42:45.933912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 889
10.9%
a 854
10.5%
. 746
9.2%
h 737
9.1%
t 726
8.9%
o 711
8.7%
m 667
8.2%
S 638
7.8%
3 638
7.8%
w 638
7.8%
Other values (17) 899
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8143
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 889
10.9%
a 854
10.5%
. 746
9.2%
h 737
9.1%
t 726
8.9%
o 711
8.7%
m 667
8.2%
S 638
7.8%
3 638
7.8%
w 638
7.8%
Other values (17) 899
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8143
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 889
10.9%
a 854
10.5%
. 746
9.2%
h 737
9.1%
t 726
8.9%
o 711
8.7%
m 667
8.2%
S 638
7.8%
3 638
7.8%
w 638
7.8%
Other values (17) 899
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8143
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 889
10.9%
a 854
10.5%
. 746
9.2%
h 737
9.1%
t 726
8.9%
o 711
8.7%
m 667
8.2%
S 638
7.8%
3 638
7.8%
w 638
7.8%
Other values (17) 899
11.0%

knowledge_bergen_belsen
Text

Missing 

Distinct5
Distinct (%)0.7%
Missing149
Missing (%)16.7%
Memory size55.0 KiB
2025-05-15T17:42:46.148355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length10
Mean length10.60268456
Min length10

Characters and Unicode

Total characters7,899
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Somewhat
2nd row3.Somewhat
3rd row3.Somewhat
4th row3.Somewhat
5th row3.Somewhat
ValueCountFrequency (%)
3.somewhat 654
74.2%
4.more 35
 
4.0%
than 35
 
4.0%
average 35
 
4.0%
2.very 34
 
3.9%
little 34
 
3.9%
5.very 12
 
1.4%
much 12
 
1.4%
1.not 10
 
1.1%
at 10
 
1.1%
2025-05-15T17:42:46.432112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 839
10.6%
a 779
9.9%
t 777
9.8%
. 745
9.4%
h 701
8.9%
o 699
8.8%
m 666
8.4%
S 654
8.3%
3 654
8.3%
w 654
8.3%
Other values (17) 731
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7899
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 839
10.6%
a 779
9.9%
t 777
9.8%
. 745
9.4%
h 701
8.9%
o 699
8.8%
m 666
8.4%
S 654
8.3%
3 654
8.3%
w 654
8.3%
Other values (17) 731
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7899
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 839
10.6%
a 779
9.9%
t 777
9.8%
. 745
9.4%
h 701
8.9%
o 699
8.8%
m 666
8.4%
S 654
8.3%
3 654
8.3%
w 654
8.3%
Other values (17) 731
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7899
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 839
10.6%
a 779
9.9%
t 777
9.8%
. 745
9.4%
h 701
8.9%
o 699
8.8%
m 666
8.4%
S 654
8.3%
3 654
8.3%
w 654
8.3%
Other values (17) 731
9.3%

knowledge_persecuted_jews
Text

Missing 

Distinct5
Distinct (%)0.7%
Missing150
Missing (%)16.8%
Memory size55.1 KiB
2025-05-15T17:42:46.592636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length10
Mean length10.83333333
Min length10

Characters and Unicode

Total characters8,060
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Somewhat
2nd row3.Somewhat
3rd row3.Somewhat
4th row3.Somewhat
5th row3.Somewhat
ValueCountFrequency (%)
3.somewhat 640
70.1%
4.more 63
 
6.9%
than 63
 
6.9%
average 63
 
6.9%
5.very 34
 
3.7%
much 34
 
3.7%
2.very 5
 
0.5%
little 5
 
0.5%
1.not 2
 
0.2%
at 2
 
0.2%
2025-05-15T17:42:47.020180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 873
10.8%
a 833
10.3%
. 744
9.2%
h 737
9.1%
t 717
8.9%
o 705
8.7%
m 674
8.4%
S 640
7.9%
3 640
7.9%
w 640
7.9%
Other values (17) 857
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 873
10.8%
a 833
10.3%
. 744
9.2%
h 737
9.1%
t 717
8.9%
o 705
8.7%
m 674
8.4%
S 640
7.9%
3 640
7.9%
w 640
7.9%
Other values (17) 857
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 873
10.8%
a 833
10.3%
. 744
9.2%
h 737
9.1%
t 717
8.9%
o 705
8.7%
m 674
8.4%
S 640
7.9%
3 640
7.9%
w 640
7.9%
Other values (17) 857
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 873
10.8%
a 833
10.3%
. 744
9.2%
h 737
9.1%
t 717
8.9%
o 705
8.7%
m 674
8.4%
S 640
7.9%
3 640
7.9%
w 640
7.9%
Other values (17) 857
10.6%

knowledge_other_persecuted_groups
Text

Missing 

Distinct5
Distinct (%)0.7%
Missing150
Missing (%)16.8%
Memory size55.1 KiB
2025-05-15T17:42:47.187656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length10
Mean length10.75672043
Min length10

Characters and Unicode

Total characters8,003
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Somewhat
2nd row3.Somewhat
3rd row3.Somewhat
4th row3.Somewhat
5th row3.Somewhat
ValueCountFrequency (%)
3.somewhat 652
72.8%
4.more 56
 
6.3%
than 56
 
6.3%
average 56
 
6.3%
5.very 23
 
2.6%
much 23
 
2.6%
2.very 10
 
1.1%
little 10
 
1.1%
1.not 3
 
0.3%
at 3
 
0.3%
2025-05-15T17:42:47.472184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 863
10.8%
a 826
10.3%
. 744
9.3%
t 734
9.2%
h 731
9.1%
o 711
8.9%
m 675
8.4%
S 652
8.1%
3 652
8.1%
w 652
8.1%
Other values (17) 763
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8003
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 863
10.8%
a 826
10.3%
. 744
9.3%
t 734
9.2%
h 731
9.1%
o 711
8.9%
m 675
8.4%
S 652
8.1%
3 652
8.1%
w 652
8.1%
Other values (17) 763
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8003
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 863
10.8%
a 826
10.3%
. 744
9.3%
t 734
9.2%
h 731
9.1%
o 711
8.9%
m 675
8.4%
S 652
8.1%
3 652
8.1%
w 652
8.1%
Other values (17) 763
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8003
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 863
10.8%
a 826
10.3%
. 744
9.3%
t 734
9.2%
h 731
9.1%
o 711
8.9%
m 675
8.4%
S 652
8.1%
3 652
8.1%
w 652
8.1%
Other values (17) 763
9.5%

known_persecuted_groups_open
Text

Missing 

Distinct49
Distinct (%)73.1%
Missing827
Missing (%)92.5%
Memory size38.5 KiB
2025-05-15T17:42:47.751931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length150
Median length71
Mean length37
Min length2

Characters and Unicode

Total characters2,479
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)64.2%

Sample

1st rowJews
2nd rowSinti and Roma__Jews__Political opponents
3rd rowSinti and Roma__Jews__Homosexuals__Disabled people
4th rowOther
5th rowSS
ValueCountFrequency (%)
and 31
 
16.3%
people 14
 
7.4%
sinti 13
 
6.8%
ss 10
 
5.3%
opponents 8
 
4.2%
roma__disabled 7
 
3.7%
roma 6
 
3.2%
jews 4
 
2.1%
jews__homosexuals__sinti 4
 
2.1%
spam 3
 
1.6%
Other values (72) 90
47.4%
2025-05-15T17:42:48.262337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 280
 
11.3%
s 221
 
8.9%
e 208
 
8.4%
o 198
 
8.0%
a 168
 
6.8%
i 157
 
6.3%
n 131
 
5.3%
l 130
 
5.2%
124
 
5.0%
t 96
 
3.9%
Other values (39) 766
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2479
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 280
 
11.3%
s 221
 
8.9%
e 208
 
8.4%
o 198
 
8.0%
a 168
 
6.8%
i 157
 
6.3%
n 131
 
5.3%
l 130
 
5.2%
124
 
5.0%
t 96
 
3.9%
Other values (39) 766
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2479
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 280
 
11.3%
s 221
 
8.9%
e 208
 
8.4%
o 198
 
8.0%
a 168
 
6.8%
i 157
 
6.3%
n 131
 
5.3%
l 130
 
5.2%
124
 
5.0%
t 96
 
3.9%
Other values (39) 766
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2479
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 280
 
11.3%
s 221
 
8.9%
e 208
 
8.4%
o 198
 
8.0%
a 168
 
6.8%
i 157
 
6.3%
n 131
 
5.3%
l 130
 
5.2%
124
 
5.0%
t 96
 
3.9%
Other values (39) 766
30.9%

technologies_used
Text

Missing 

Distinct6
Distinct (%)7.7%
Missing816
Missing (%)91.3%
Memory size38.4 KiB
2025-05-15T17:42:48.416016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length61
Median length44
Mean length29.19230769
Min length15

Characters and Unicode

Total characters2,277
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVirtual Reality (VR)
2nd row3D model viewer
3rd row3D model viewer
4th rowVirtual Reality (VR)
5th rowVirtual Reality (VR)__3D model viewer
ValueCountFrequency (%)
reality 69
21.7%
model 51
16.0%
viewer 51
16.0%
virtual 28
8.8%
3d 26
 
8.2%
vr)__3d 25
 
7.9%
augmented 24
 
7.5%
vr 20
 
6.3%
ar)__virtual 17
 
5.3%
ar 7
 
2.2%
2025-05-15T17:42:48.671655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 270
 
11.9%
240
 
10.5%
l 165
 
7.2%
i 165
 
7.2%
t 138
 
6.1%
R 138
 
6.1%
a 114
 
5.0%
r 96
 
4.2%
V 90
 
4.0%
_ 84
 
3.7%
Other values (14) 777
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 270
 
11.9%
240
 
10.5%
l 165
 
7.2%
i 165
 
7.2%
t 138
 
6.1%
R 138
 
6.1%
a 114
 
5.0%
r 96
 
4.2%
V 90
 
4.0%
_ 84
 
3.7%
Other values (14) 777
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 270
 
11.9%
240
 
10.5%
l 165
 
7.2%
i 165
 
7.2%
t 138
 
6.1%
R 138
 
6.1%
a 114
 
5.0%
r 96
 
4.2%
V 90
 
4.0%
_ 84
 
3.7%
Other values (14) 777
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 270
 
11.9%
240
 
10.5%
l 165
 
7.2%
i 165
 
7.2%
t 138
 
6.1%
R 138
 
6.1%
a 114
 
5.0%
r 96
 
4.2%
V 90
 
4.0%
_ 84
 
3.7%
Other values (14) 777
34.1%

videogame_frequency
Text

Distinct21
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size55.4 KiB
2025-05-15T17:42:48.815098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.412751678
Min length1

Characters and Unicode

Total characters5,733
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.9%

Sample

1st rowInvalid
2nd rowInvalid
3rd rowInvalid
4th rowInvalid
5th rowInvalid
ValueCountFrequency (%)
invalid 801
89.6%
0 34
 
3.8%
1 10
 
1.1%
10 8
 
0.9%
2 7
 
0.8%
3 5
 
0.6%
20 5
 
0.6%
5 4
 
0.4%
4 3
 
0.3%
8 3
 
0.3%
Other values (11) 14
 
1.6%
2025-05-15T17:42:49.092063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 803
14.0%
I 801
14.0%
n 801
14.0%
v 801
14.0%
l 801
14.0%
i 801
14.0%
d 801
14.0%
0 52
 
0.9%
1 21
 
0.4%
2 15
 
0.3%
Other values (11) 36
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 803
14.0%
I 801
14.0%
n 801
14.0%
v 801
14.0%
l 801
14.0%
i 801
14.0%
d 801
14.0%
0 52
 
0.9%
1 21
 
0.4%
2 15
 
0.3%
Other values (11) 36
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 803
14.0%
I 801
14.0%
n 801
14.0%
v 801
14.0%
l 801
14.0%
i 801
14.0%
d 801
14.0%
0 52
 
0.9%
1 21
 
0.4%
2 15
 
0.3%
Other values (11) 36
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 803
14.0%
I 801
14.0%
n 801
14.0%
v 801
14.0%
l 801
14.0%
i 801
14.0%
d 801
14.0%
0 52
 
0.9%
1 21
 
0.4%
2 15
 
0.3%
Other values (11) 36
 
0.6%

felt_part_of_activity
Text

Missing 

Distinct4
Distinct (%)0.6%
Missing172
Missing (%)19.2%
Memory size54.7 KiB
2025-05-15T17:42:49.223156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length11
Mean length11.01246537
Min length7

Characters and Unicode

Total characters7,951
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 713
98.3%
1.strongly 3
 
0.4%
disagree 3
 
0.4%
4.agree 3
 
0.4%
2.disagree 3
 
0.4%
2025-05-15T17:42:49.467603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2142
26.9%
e 1444
18.2%
. 722
 
9.1%
i 719
 
9.0%
n 716
 
9.0%
U 713
 
9.0%
3 713
 
9.0%
c 713
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (13) 45
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7951
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2142
26.9%
e 1444
18.2%
. 722
 
9.1%
i 719
 
9.0%
n 716
 
9.0%
U 713
 
9.0%
3 713
 
9.0%
c 713
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (13) 45
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7951
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2142
26.9%
e 1444
18.2%
. 722
 
9.1%
i 719
 
9.0%
n 716
 
9.0%
U 713
 
9.0%
3 713
 
9.0%
c 713
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (13) 45
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7951
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2142
26.9%
e 1444
18.2%
. 722
 
9.1%
i 719
 
9.0%
n 716
 
9.0%
U 713
 
9.0%
3 713
 
9.0%
c 713
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (13) 45
 
0.6%

involvement_over_irrelevant_thoughts
Text

Missing 

Distinct4
Distinct (%)0.6%
Missing173
Missing (%)19.4%
Memory size54.6 KiB
2025-05-15T17:42:49.607552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length11
Mean length10.98613037
Min length7

Characters and Unicode

Total characters7,921
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 714
98.9%
4.agree 4
 
0.6%
2.disagree 2
 
0.3%
1.strongly 1
 
0.1%
disagree 1
 
0.1%
2025-05-15T17:42:49.867021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2143
27.1%
e 1442
18.2%
. 721
 
9.1%
i 717
 
9.1%
n 715
 
9.0%
U 714
 
9.0%
3 714
 
9.0%
c 714
 
9.0%
g 8
 
0.1%
r 8
 
0.1%
Other values (13) 25
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2143
27.1%
e 1442
18.2%
. 721
 
9.1%
i 717
 
9.1%
n 715
 
9.0%
U 714
 
9.0%
3 714
 
9.0%
c 714
 
9.0%
g 8
 
0.1%
r 8
 
0.1%
Other values (13) 25
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2143
27.1%
e 1442
18.2%
. 721
 
9.1%
i 717
 
9.1%
n 715
 
9.0%
U 714
 
9.0%
3 714
 
9.0%
c 714
 
9.0%
g 8
 
0.1%
r 8
 
0.1%
Other values (13) 25
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2143
27.1%
e 1442
18.2%
. 721
 
9.1%
i 717
 
9.1%
n 715
 
9.0%
U 714
 
9.0%
3 714
 
9.0%
c 714
 
9.0%
g 8
 
0.1%
r 8
 
0.1%
Other values (13) 25
 
0.3%

experienced_activity_feeling
Text

Missing 

Distinct4
Distinct (%)0.6%
Missing172
Missing (%)19.2%
Memory size54.7 KiB
2025-05-15T17:42:49.999499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length11
Mean length11.03462604
Min length10

Characters and Unicode

Total characters7,967
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 714
98.3%
2.disagree 4
 
0.6%
1.strongly 3
 
0.4%
disagree 3
 
0.4%
5.strongly 1
 
0.1%
agree 1
 
0.1%
2025-05-15T17:42:50.280919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2145
26.9%
e 1444
18.1%
. 722
 
9.1%
i 721
 
9.0%
n 718
 
9.0%
3 714
 
9.0%
U 714
 
9.0%
c 714
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (12) 51
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7967
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2145
26.9%
e 1444
18.1%
. 722
 
9.1%
i 721
 
9.0%
n 718
 
9.0%
3 714
 
9.0%
U 714
 
9.0%
c 714
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (12) 51
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7967
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2145
26.9%
e 1444
18.1%
. 722
 
9.1%
i 721
 
9.0%
n 718
 
9.0%
3 714
 
9.0%
U 714
 
9.0%
c 714
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (12) 51
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7967
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2145
26.9%
e 1444
18.1%
. 722
 
9.1%
i 721
 
9.0%
n 718
 
9.0%
3 714
 
9.0%
U 714
 
9.0%
c 714
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (12) 51
 
0.6%

lost_track_of_time
Text

Missing 

Distinct5
Distinct (%)0.7%
Missing172
Missing (%)19.2%
Memory size54.7 KiB
2025-05-15T17:42:50.424921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length11
Mean length11.00969529
Min length7

Characters and Unicode

Total characters7,949
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.4%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 717
99.0%
2.disagree 2
 
0.3%
1.strongly 1
 
0.1%
disagree 1
 
0.1%
4.agree 1
 
0.1%
5.strongly 1
 
0.1%
agree 1
 
0.1%
2025-05-15T17:42:50.702991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2152
27.1%
e 1444
18.2%
. 722
 
9.1%
i 720
 
9.1%
n 719
 
9.0%
3 717
 
9.0%
U 717
 
9.0%
c 717
 
9.0%
r 7
 
0.1%
g 7
 
0.1%
Other values (14) 27
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2152
27.1%
e 1444
18.2%
. 722
 
9.1%
i 720
 
9.1%
n 719
 
9.0%
3 717
 
9.0%
U 717
 
9.0%
c 717
 
9.0%
r 7
 
0.1%
g 7
 
0.1%
Other values (14) 27
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2152
27.1%
e 1444
18.2%
. 722
 
9.1%
i 720
 
9.1%
n 719
 
9.0%
3 717
 
9.0%
U 717
 
9.0%
c 717
 
9.0%
r 7
 
0.1%
g 7
 
0.1%
Other values (14) 27
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2152
27.1%
e 1444
18.2%
. 722
 
9.1%
i 720
 
9.1%
n 719
 
9.0%
3 717
 
9.0%
U 717
 
9.0%
c 717
 
9.0%
r 7
 
0.1%
g 7
 
0.1%
Other values (14) 27
 
0.3%

was_interesting
Text

Missing 

Distinct4
Distinct (%)0.6%
Missing174
Missing (%)19.5%
Memory size54.6 KiB
2025-05-15T17:42:50.842173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length11
Mean length11.00972222
Min length7

Characters and Unicode

Total characters7,927
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 713
98.5%
agree 4
 
0.6%
5.strongly 3
 
0.4%
4.agree 3
 
0.4%
5.totally 1
 
0.1%
2025-05-15T17:42:51.130637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2139
27.0%
e 1440
18.2%
. 720
 
9.1%
n 716
 
9.0%
3 713
 
9.0%
U 713
 
9.0%
c 713
 
9.0%
i 713
 
9.0%
r 10
 
0.1%
g 10
 
0.1%
Other values (11) 40
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2139
27.0%
e 1440
18.2%
. 720
 
9.1%
n 716
 
9.0%
3 713
 
9.0%
U 713
 
9.0%
c 713
 
9.0%
i 713
 
9.0%
r 10
 
0.1%
g 10
 
0.1%
Other values (11) 40
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2139
27.0%
e 1440
18.2%
. 720
 
9.1%
n 716
 
9.0%
3 713
 
9.0%
U 713
 
9.0%
c 713
 
9.0%
i 713
 
9.0%
r 10
 
0.1%
g 10
 
0.1%
Other values (11) 40
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2139
27.0%
e 1440
18.2%
. 720
 
9.1%
n 716
 
9.0%
3 713
 
9.0%
U 713
 
9.0%
c 713
 
9.0%
i 713
 
9.0%
r 10
 
0.1%
g 10
 
0.1%
Other values (11) 40
 
0.5%

left_weak_impression
Text

Missing 

Distinct4
Distinct (%)0.6%
Missing174
Missing (%)19.5%
Memory size54.6 KiB
2025-05-15T17:42:51.274869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length11
Mean length11.01388889
Min length10

Characters and Unicode

Total characters7,930
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 715
99.0%
2.disagree 3
 
0.4%
5.strongly 1
 
0.1%
agree 1
 
0.1%
1.strongly 1
 
0.1%
disagree 1
 
0.1%
2025-05-15T17:42:51.522662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2146
27.1%
e 1440
18.2%
. 720
 
9.1%
i 719
 
9.1%
n 717
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 7
 
0.1%
r 7
 
0.1%
Other values (12) 29
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2146
27.1%
e 1440
18.2%
. 720
 
9.1%
i 719
 
9.1%
n 717
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 7
 
0.1%
r 7
 
0.1%
Other values (12) 29
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2146
27.1%
e 1440
18.2%
. 720
 
9.1%
i 719
 
9.1%
n 717
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 7
 
0.1%
r 7
 
0.1%
Other values (12) 29
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2146
27.1%
e 1440
18.2%
. 720
 
9.1%
i 719
 
9.1%
n 717
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 7
 
0.1%
r 7
 
0.1%
Other values (12) 29
 
0.4%

was_boring
Text

Missing 

Distinct4
Distinct (%)0.6%
Missing174
Missing (%)19.5%
Memory size54.7 KiB
2025-05-15T17:42:51.662668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length11
Mean length11.05833333
Min length10

Characters and Unicode

Total characters7,962
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 711
97.9%
1.strongly 5
 
0.7%
disagree 5
 
0.7%
2.disagree 3
 
0.4%
5.strongly 1
 
0.1%
agree 1
 
0.1%
2025-05-15T17:42:51.911165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2138
26.9%
e 1440
18.1%
. 720
 
9.0%
i 719
 
9.0%
n 717
 
9.0%
3 711
 
8.9%
U 711
 
8.9%
c 711
 
8.9%
g 15
 
0.2%
r 15
 
0.2%
Other values (12) 65
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2138
26.9%
e 1440
18.1%
. 720
 
9.0%
i 719
 
9.0%
n 717
 
9.0%
3 711
 
8.9%
U 711
 
8.9%
c 711
 
8.9%
g 15
 
0.2%
r 15
 
0.2%
Other values (12) 65
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2138
26.9%
e 1440
18.1%
. 720
 
9.0%
i 719
 
9.0%
n 717
 
9.0%
3 711
 
8.9%
U 711
 
8.9%
c 711
 
8.9%
g 15
 
0.2%
r 15
 
0.2%
Other values (12) 65
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2138
26.9%
e 1440
18.1%
. 720
 
9.0%
i 719
 
9.0%
n 717
 
9.0%
3 711
 
8.9%
U 711
 
8.9%
c 711
 
8.9%
g 15
 
0.2%
r 15
 
0.2%
Other values (12) 65
 
0.8%

thought_innovative
Text

Missing 

Distinct4
Distinct (%)0.6%
Missing174
Missing (%)19.5%
Memory size54.6 KiB
2025-05-15T17:42:52.065359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length11
Mean length11.02638889
Min length7

Characters and Unicode

Total characters7,939
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 715
98.8%
5.strongly 3
 
0.4%
agree 3
 
0.4%
4.agree 1
 
0.1%
1.strongly 1
 
0.1%
disagree 1
 
0.1%
2025-05-15T17:42:52.342274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2146
27.0%
e 1440
18.1%
. 720
 
9.1%
n 719
 
9.1%
i 716
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 9
 
0.1%
r 9
 
0.1%
Other values (12) 35
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7939
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2146
27.0%
e 1440
18.1%
. 720
 
9.1%
n 719
 
9.1%
i 716
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 9
 
0.1%
r 9
 
0.1%
Other values (12) 35
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7939
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2146
27.0%
e 1440
18.1%
. 720
 
9.1%
n 719
 
9.1%
i 716
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 9
 
0.1%
r 9
 
0.1%
Other values (12) 35
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7939
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2146
27.0%
e 1440
18.1%
. 720
 
9.1%
n 719
 
9.1%
i 716
 
9.0%
3 715
 
9.0%
U 715
 
9.0%
c 715
 
9.0%
g 9
 
0.1%
r 9
 
0.1%
Other values (12) 35
 
0.4%

understood_camp_appearance
Text

Missing 

Distinct5
Distinct (%)0.7%
Missing173
Missing (%)19.4%
Memory size54.6 KiB
2025-05-15T17:42:52.483232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length11
Mean length11
Min length7

Characters and Unicode

Total characters7,931
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 711
98.1%
4.agree 4
 
0.6%
agree 4
 
0.6%
2.disagree 2
 
0.3%
5.strongly 2
 
0.3%
5.totally 2
 
0.3%
2025-05-15T17:42:52.722178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
n 713
 
9.0%
i 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 52
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
n 713
 
9.0%
i 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 52
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
n 713
 
9.0%
i 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 52
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
n 713
 
9.0%
i 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 52
 
0.7%

understood_life_in_camp
Text

Missing 

Distinct6
Distinct (%)0.8%
Missing173
Missing (%)19.4%
Memory size54.7 KiB
2025-05-15T17:42:52.903421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length11
Mean length11.0221914
Min length7

Characters and Unicode

Total characters7,947
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 712
98.1%
agree 4
 
0.6%
2.disagree 2
 
0.3%
5.strongly 2
 
0.3%
4.agree 2
 
0.3%
5.totally 2
 
0.3%
1.strongly 1
 
0.1%
disagree 1
 
0.1%
2025-05-15T17:42:53.209927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2137
26.9%
e 1442
18.1%
. 721
 
9.1%
i 715
 
9.0%
n 715
 
9.0%
3 712
 
9.0%
U 712
 
9.0%
c 712
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (15) 57
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7947
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2137
26.9%
e 1442
18.1%
. 721
 
9.1%
i 715
 
9.0%
n 715
 
9.0%
3 712
 
9.0%
U 712
 
9.0%
c 712
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (15) 57
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7947
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2137
26.9%
e 1442
18.1%
. 721
 
9.1%
i 715
 
9.0%
n 715
 
9.0%
3 712
 
9.0%
U 712
 
9.0%
c 712
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (15) 57
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7947
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2137
26.9%
e 1442
18.1%
. 721
 
9.1%
i 715
 
9.0%
n 715
 
9.0%
3 712
 
9.0%
U 712
 
9.0%
c 712
 
9.0%
g 12
 
0.2%
r 12
 
0.2%
Other values (15) 57
 
0.7%

understood_camp_function
Text

Missing 

Distinct5
Distinct (%)0.7%
Missing173
Missing (%)19.4%
Memory size54.6 KiB
2025-05-15T17:42:53.403660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length11
Mean length11.00416089
Min length7

Characters and Unicode

Total characters7,934
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 711
98.1%
agree 4
 
0.6%
2.disagree 3
 
0.4%
4.agree 3
 
0.4%
5.strongly 2
 
0.3%
5.totally 2
 
0.3%
2025-05-15T17:42:53.668802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
i 714
 
9.0%
n 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 54
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7934
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
i 714
 
9.0%
n 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 54
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7934
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
i 714
 
9.0%
n 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 54
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7934
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2133
26.9%
e 1442
18.2%
. 721
 
9.1%
i 714
 
9.0%
n 713
 
9.0%
3 711
 
9.0%
U 711
 
9.0%
c 711
 
9.0%
r 12
 
0.2%
g 12
 
0.2%
Other values (14) 54
 
0.7%

felt_sympathetic_to_victims
Text

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing175
Missing (%)19.6%
Memory size54.6 KiB
2025-05-15T17:42:53.839478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters7,909
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 719
100.0%
2025-05-15T17:42:54.110166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

impact_on_own_life
Text

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing175
Missing (%)19.6%
Memory size54.6 KiB
2025-05-15T17:42:54.235238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters7,909
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 719
100.0%
2025-05-15T17:42:54.473253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

impact_on_society
Text

Constant  Missing 

Distinct1
Distinct (%)0.1%
Missing175
Missing (%)19.6%
Memory size54.6 KiB
2025-05-15T17:42:54.597735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters7,909
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 719
100.0%
2025-05-15T17:42:54.829337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2157
27.3%
e 1438
18.2%
3 719
 
9.1%
. 719
 
9.1%
n 719
 
9.1%
U 719
 
9.1%
c 719
 
9.1%
i 719
 
9.1%

impact_society_details
Unsupported

Missing  Rejected  Unsupported 

Missing894
Missing (%)100.0%
Memory size14.0 KiB

want_to_share_learning
Text

Missing 

Distinct6
Distinct (%)0.8%
Missing169
Missing (%)18.9%
Memory size54.8 KiB
2025-05-15T17:42:54.998010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length11
Mean length11.01517241
Min length7

Characters and Unicode

Total characters7,986
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 702
95.4%
4.agree 11
 
1.5%
agree 10
 
1.4%
5.strongly 8
 
1.1%
5.totally 2
 
0.3%
1.strongly 1
 
0.1%
disagree 1
 
0.1%
2.disagree 1
 
0.1%
2025-05-15T17:42:55.242321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2107
26.4%
e 1450
18.2%
. 725
 
9.1%
n 711
 
8.9%
i 704
 
8.8%
3 702
 
8.8%
U 702
 
8.8%
c 702
 
8.8%
g 32
 
0.4%
r 32
 
0.4%
Other values (15) 119
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2107
26.4%
e 1450
18.2%
. 725
 
9.1%
n 711
 
8.9%
i 704
 
8.8%
3 702
 
8.8%
U 702
 
8.8%
c 702
 
8.8%
g 32
 
0.4%
r 32
 
0.4%
Other values (15) 119
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2107
26.4%
e 1450
18.2%
. 725
 
9.1%
n 711
 
8.9%
i 704
 
8.8%
3 702
 
8.8%
U 702
 
8.8%
c 702
 
8.8%
g 32
 
0.4%
r 32
 
0.4%
Other values (15) 119
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2107
26.4%
e 1450
18.2%
. 725
 
9.1%
n 711
 
8.9%
i 704
 
8.8%
3 702
 
8.8%
U 702
 
8.8%
c 702
 
8.8%
g 32
 
0.4%
r 32
 
0.4%
Other values (15) 119
 
1.5%

plan_to_learn_more
Text

Missing 

Distinct6
Distinct (%)0.8%
Missing169
Missing (%)18.9%
Memory size54.8 KiB
2025-05-15T17:42:55.425776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length11
Mean length11.04
Min length7

Characters and Unicode

Total characters8,004
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.Undecided
2nd row3.Undecided
3rd row3.Undecided
4th row3.Undecided
5th row3.Undecided
ValueCountFrequency (%)
3.undecided 705
95.8%
agree 9
 
1.2%
4.agree 7
 
1.0%
5.strongly 7
 
1.0%
2.disagree 2
 
0.3%
1.strongly 2
 
0.3%
disagree 2
 
0.3%
5.totally 2
 
0.3%
2025-05-15T17:42:55.689673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 2117
26.4%
e 1450
18.1%
. 725
 
9.1%
n 714
 
8.9%
i 709
 
8.9%
3 705
 
8.8%
U 705
 
8.8%
c 705
 
8.8%
g 29
 
0.4%
r 29
 
0.4%
Other values (15) 116
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 2117
26.4%
e 1450
18.1%
. 725
 
9.1%
n 714
 
8.9%
i 709
 
8.9%
3 705
 
8.8%
U 705
 
8.8%
c 705
 
8.8%
g 29
 
0.4%
r 29
 
0.4%
Other values (15) 116
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 2117
26.4%
e 1450
18.1%
. 725
 
9.1%
n 714
 
8.9%
i 709
 
8.9%
3 705
 
8.8%
U 705
 
8.8%
c 705
 
8.8%
g 29
 
0.4%
r 29
 
0.4%
Other values (15) 116
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 2117
26.4%
e 1450
18.1%
. 725
 
9.1%
n 714
 
8.9%
i 709
 
8.9%
3 705
 
8.8%
U 705
 
8.8%
c 705
 
8.8%
g 29
 
0.4%
r 29
 
0.4%
Other values (15) 116
 
1.4%

additional_feedback
Text

Missing 

Distinct3
Distinct (%)100.0%
Missing891
Missing (%)99.7%
Memory size35.0 KiB
2025-05-15T17:42:55.877929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length61
Median length4
Mean length23
Min length4

Characters and Unicode

Total characters69
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowSpam
2nd rowUsability comment: somewhat tricky to use, would retry online
3rd rowdie
ValueCountFrequency (%)
spam 1
9.1%
usability 1
9.1%
comment 1
9.1%
somewhat 1
9.1%
tricky 1
9.1%
to 1
9.1%
use 1
9.1%
would 1
9.1%
retry 1
9.1%
online 1
9.1%
2025-05-15T17:42:56.174985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9
13.0%
t 6
 
8.7%
e 6
 
8.7%
i 5
 
7.2%
o 5
 
7.2%
m 4
 
5.8%
s 3
 
4.3%
a 3
 
4.3%
y 3
 
4.3%
l 3
 
4.3%
Other values (14) 22
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9
13.0%
t 6
 
8.7%
e 6
 
8.7%
i 5
 
7.2%
o 5
 
7.2%
m 4
 
5.8%
s 3
 
4.3%
a 3
 
4.3%
y 3
 
4.3%
l 3
 
4.3%
Other values (14) 22
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9
13.0%
t 6
 
8.7%
e 6
 
8.7%
i 5
 
7.2%
o 5
 
7.2%
m 4
 
5.8%
s 3
 
4.3%
a 3
 
4.3%
y 3
 
4.3%
l 3
 
4.3%
Other values (14) 22
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9
13.0%
t 6
 
8.7%
e 6
 
8.7%
i 5
 
7.2%
o 5
 
7.2%
m 4
 
5.8%
s 3
 
4.3%
a 3
 
4.3%
y 3
 
4.3%
l 3
 
4.3%
Other values (14) 22
31.9%

email
Text

Missing 

Distinct7
Distinct (%)100.0%
Missing887
Missing (%)99.2%
Memory size35.2 KiB
2025-05-15T17:42:56.354654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length21
Mean length19.71428571
Min length5

Characters and Unicode

Total characters138
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st rowparrier@gmail.com
2nd rowbambusapfel@gmail.com
3rd rowkayleemsrielshmar205@gmail.com
4th rowWer@gy-her.de
5th rowmichel.bonkowski@ewe.net
ValueCountFrequency (%)
parrier@gmail.com 1
14.3%
bambusapfel@gmail.com 1
14.3%
kayleemsrielshmar205@gmail.com 1
14.3%
wer@gy-her.de 1
14.3%
michel.bonkowski@ewe.net 1
14.3%
mariazaharia2002@yahoo.co.uk 1
14.3%
roger 1
14.3%
2025-05-15T17:42:56.624717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14
 
10.1%
e 13
 
9.4%
r 11
 
8.0%
m 11
 
8.0%
i 9
 
6.5%
o 9
 
6.5%
. 8
 
5.8%
l 7
 
5.1%
@ 6
 
4.3%
c 5
 
3.6%
Other values (19) 45
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 14
 
10.1%
e 13
 
9.4%
r 11
 
8.0%
m 11
 
8.0%
i 9
 
6.5%
o 9
 
6.5%
. 8
 
5.8%
l 7
 
5.1%
@ 6
 
4.3%
c 5
 
3.6%
Other values (19) 45
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 14
 
10.1%
e 13
 
9.4%
r 11
 
8.0%
m 11
 
8.0%
i 9
 
6.5%
o 9
 
6.5%
. 8
 
5.8%
l 7
 
5.1%
@ 6
 
4.3%
c 5
 
3.6%
Other values (19) 45
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 14
 
10.1%
e 13
 
9.4%
r 11
 
8.0%
m 11
 
8.0%
i 9
 
6.5%
o 9
 
6.5%
. 8
 
5.8%
l 7
 
5.1%
@ 6
 
4.3%
c 5
 
3.6%
Other values (19) 45
32.6%